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add losses

ppcls/losses/celoss.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.nn.functional as F
+
+__all__ = ['CELoss', 'JSDivLoss', 'KLDivLoss']
+
+
+class Loss(object):
+    """
+    Loss
+    """
+    def __init__(self, class_dim=1000, epsilon=None):
+        assert class_dim > 1, "class_dim=%d is not larger than 1" % (class_dim)
+        self._class_dim = class_dim
+        if epsilon is not None and epsilon >= 0.0 and epsilon <= 1.0:
+            self._epsilon = epsilon
+            self._label_smoothing = True  #use label smoothing.(Actually, it is softmax label)
+        else:
+            self._epsilon = None
+            self._label_smoothing = False
+
+    #do label_smoothing
+    def _labelsmoothing(self, target):
+        if target.shape[-1] != self._class_dim:
+            one_hot_target = F.one_hot(target, self._class_dim)  #do ont hot(23,34,46)-> 3 * _class_dim
+        else:
+            one_hot_target = target
+
+        #do label_smooth
+        soft_target = F.label_smooth(one_hot_target, epsilon=self._epsilon)   #(1 - epsilon) * input + eposilon / K.
+        soft_target = paddle.reshape(soft_target, shape=[-1, self._class_dim])
+        return soft_target
+
+    def _crossentropy(self, input, target, use_pure_fp16=False):
+        if self._label_smoothing:
+            target = self._labelsmoothing(target)
+            input = -F.log_softmax(input, axis=-1)      #softmax and do log
+            cost = paddle.sum(target * input, axis=-1)  #sum  
+        else:
+            cost = F.cross_entropy(input=input, label=target) 
+
+        if use_pure_fp16:
+            avg_cost = paddle.sum(cost)
+        else:
+            avg_cost = paddle.mean(cost)
+        return avg_cost
+
+    def _kldiv(self, input, target, name=None):
+        eps = 1.0e-10
+        cost = target * paddle.log(
+            (target + eps) / (input + eps)) * self._class_dim
+        return cost
+
+    def _jsdiv(self, input, target):  #so the input and target is the fc output; no softmax
+        input = F.softmax(input)
+        target = F.softmax(target) 
+
+        #two distribution
+        cost = self._kldiv(input, target) + self._kldiv(target, input)
+        cost = cost / 2
+        avg_cost = paddle.mean(cost)
+        return avg_cost
+
+    def __call__(self, input, target):
+        pass
+
+
+class CELoss(Loss):
+    """
+    Cross entropy loss
+    """
+
+    def __init__(self, class_dim=1000, epsilon=None):
+        super(CELoss, self).__init__(class_dim, epsilon)
+
+    def __call__(self, input, target, use_pure_fp16=False):
+        logits = input["logits"]
+        cost = self._crossentropy(logits, target, use_pure_fp16)
+        return {"CELoss": cost}
+
+class JSDivLoss(Loss):
+    """
+    JSDiv loss
+    """
+    def __init__(self, class_dim=1000, epsilon=None):
+        super(JSDivLoss, self).__init__(class_dim, epsilon)
+
+    def __call__(self, input, target):
+        cost = self._jsdiv(input, target)
+        return cost
+
+
+class KLDivLoss(paddle.nn.Layer):
+    def __init__(self):
+        super(KLDivLoss, self).__init__()
+
+    def __call__(self, p, q, is_logit=True):
+        if is_logit:
+            p = paddle.nn.functional.softmax(p)
+            q = paddle.nn.functional.softmax(q)
+        return -(p * paddle.log(q + 1e-8)).sum(1).mean()
\ No newline at end of file

ppcls/losses/centerloss.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+class CenterLoss(nn.Layer):
+    def __init__(self, num_classes=5013, feat_dim=2048):
+        super(CenterLoss, self).__init__()
+        self.num_classes = num_classes
+        self.feat_dim = feat_dim
+        self.centers  = paddle.randn(shape=[self.num_classes, self.feat_dim]).astype("float64")  #random center
+
+    def __call__(self, input, target):
+        """
+        inputs: network output: {"features: xxx", "logits": xxxx}
+        target: image label
+        """
+        feats = input["features"]
+        labels = target
+        batch_size = feats.shape[0]
+
+        #calc feat * feat   
+        dist1 = paddle.sum(paddle.square(feats), axis=1, keepdim=True)
+        dist1 = paddle.expand(dist1, [batch_size, self.num_classes])  
+
+        #dist2 of centers
+        dist2 = paddle.sum(paddle.square(self.centers), axis=1, keepdim=True)   #num_classes
+        dist2 = paddle.expand(dist2, [self.num_classes, batch_size]).astype("float64")
+        dist2 = paddle.transpose(dist2, [1, 0])
+
+        #first x * x + y * y
+        distmat = paddle.add(dist1, dist2)
+        tmp = paddle.matmul(feats,  paddle.transpose(self.centers, [1, 0]))
+        distmat = distmat -  2.0 * tmp
+
+        #generate the mask
+        classes = paddle.arange(self.num_classes).astype("int64")
+        labels  = paddle.expand(paddle.unsqueeze(labels, 1), (batch_size, self.num_classes))
+        mask    = paddle.equal(paddle.expand(classes, [batch_size, self.num_classes]), labels).astype("float64")  #get mask
+
+        dist = paddle.multiply(distmat,  mask)
+        loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
+
+        return {'CenterLoss': loss}
\ No newline at end of file

ppcls/losses/comfunc.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+def rerange_index(batch_size, samples_each_class):
+    tmp = np.arange(0, batch_size * batch_size) 
+    tmp = tmp.reshape(-1, batch_size) 
+    rerange_index = []
+
+    for i in range(batch_size):
+        step = i // samples_each_class
+        start = step * samples_each_class
+        end   = (step + 1) * samples_each_class
+
+        pos_idx = []   
+        neg_idx = []   
+        for j, k in enumerate(tmp[i]):
+            if j >= start and j < end:
+                if j == i:
+                    pos_idx.insert(0, k)
+                else:
+                    pos_idx.append(k)  
+            else:
+                neg_idx.append(k)  
+        rerange_index += (pos_idx + neg_idx)
+
+    rerange_index = np.array(rerange_index).astype(np.int32)
+    return rerange_index
\ No newline at end of file

ppcls/losses/emlloss.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle
+import numpy as np
+from .comfunc import rerange_index
+
+class EmlLoss(paddle.nn.Layer):
+    def __init__(self, batch_size = 40, samples_each_class = 2):
+        super(EmlLoss, self).__init__()
+        assert(batch_size % samples_each_class == 0)
+        self.samples_each_class = samples_each_class
+        self.batch_size   = batch_size
+        self.rerange_index      = rerange_index(batch_size, samples_each_class)
+        self.thresh = 20.0
+        self.beta   = 100000
+        
+    def surrogate_function(self, beta, theta, bias):
+        x = theta * paddle.exp(bias) 
+        output = paddle.log(1 + beta * x) / math.log(1 + beta)
+        return output
+
+    def surrogate_function_approximate(self, beta, theta, bias):
+        output = (paddle.log(theta) + bias + math.log(beta)) / math.log(1+beta)
+        return output
+
+    def surrogate_function_stable(self, beta, theta, target, thresh):
+        max_gap = paddle.to_tensor(thresh, dtype='float32')
+        max_gap.stop_gradient = True
+        
+        target_max = paddle.maximum(target, max_gap)
+        target_min = paddle.minimum(target, max_gap)
+        
+        loss1 = self.surrogate_function(beta, theta, target_min)
+        loss2 = self.surrogate_function_approximate(beta, theta, target_max)
+        bias  = self.surrogate_function(beta, theta, max_gap)
+        loss  = loss1 + loss2 - bias
+        return loss
+
+    def forward(self, input, target=None):
+        features = input["features"]
+        samples_each_class = self.samples_each_class
+        batch_size         = self.batch_size
+        rerange_index      = self.rerange_index
+        
+        #calc distance
+        diffs = paddle.unsqueeze(features, axis=1) - paddle.unsqueeze(features, axis=0)
+        similary_matrix =  paddle.sum(paddle.square(diffs), axis=-1)   
+    
+        tmp = paddle.reshape(similary_matrix, shape = [-1, 1]) 
+        rerange_index = paddle.to_tensor(rerange_index)
+        tmp = paddle.gather(tmp, index=rerange_index)   
+        similary_matrix = paddle.reshape(tmp, shape=[-1, batch_size])  
+        
+        ignore, pos, neg = paddle.split(similary_matrix, num_or_sections= [1, 
+            samples_each_class - 1, batch_size - samples_each_class], axis = 1)
+        ignore.stop_gradient = True 
+
+        pos_max = paddle.max(pos, axis=1, keepdim=True)
+        pos = paddle.exp(pos - pos_max)
+        pos_mean = paddle.mean(pos, axis=1, keepdim=True)
+
+        neg_min = paddle.min(neg, axis=1, keepdim=True)
+        neg = paddle.exp(neg_min - neg)
+        neg_mean = paddle.mean(neg, axis=1, keepdim=True)
+        
+        bias = pos_max - neg_min
+        theta = paddle.multiply(neg_mean, pos_mean)
+
+        loss = self.surrogate_function_stable(self.beta, theta, bias, self.thresh)
+        loss = paddle.mean(loss)
+        return {"emlloss": loss}
+
+if __name__=="__main__":
+    
+    metric = EmlLoss()
+
+    np.random.seed(1)
+    features = np.random.randn(40, 32)
+    features = paddle.to_tensor(features, dtype="float32")
+    print(features)    
+    
+    loss = metric(features)
+    print(loss)
\ No newline at end of file

ppcls/losses/msmloss.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+from .comfunc import rerange_index
+
+class MSMLoss(paddle.nn.Layer):
+    """
+    MSMLoss Loss, based on triplet loss. USE P * K samples.
+    the batch size is fixed. Batch_size = P * K;  but the K may vary between batches.
+    same label gather together
+    
+            supported_metrics = [
+            'euclidean',
+            'sqeuclidean',
+            'cityblock',
+        ]
+    only consider samples_each_class = 2
+    """
+    def __init__(self, batch_size = 120, samples_each_class=2,  margin=0.1):
+        super(MSMLoss, self).__init__()
+        self.margin = margin
+        self.samples_each_class = samples_each_class
+        self.batch_size         = batch_size
+        self.rerange_index      = rerange_index(batch_size, samples_each_class)
+
+    def forward(self, input, target=None):
+        #normalization 
+        features = input["features"]
+        features = self._nomalize(features)
+        samples_each_class = self.samples_each_class
+        rerange_index      = paddle.to_tensor(self.rerange_index)
+
+        #calc sm
+        diffs = paddle.unsqueeze(features, axis=1) - paddle.unsqueeze(features, axis=0)
+        similary_matrix =  paddle.sum(paddle.square(diffs), axis=-1)
+        
+        #rerange 
+        tmp = paddle.reshape(similary_matrix, shape = [-1, 1]) 
+        tmp = paddle.gather(tmp, index=rerange_index)   
+        similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])  
+        
+        #split
+        ignore, pos, neg = paddle.split(similary_matrix, num_or_sections= [1, 
+            samples_each_class - 1, -1], axis = 1)
+        ignore.stop_gradient = True   
+
+        hard_pos = paddle.max(pos)   
+        hard_neg = paddle.min(neg)
+
+        loss = hard_pos + self.margin - hard_neg
+        loss = paddle.nn.ReLU()(loss)  
+        return {"msmloss": loss}
+
+    def _nomalize(self, input):
+        input_norm = paddle.sqrt(paddle.sum(paddle.square(input), axis=1, keepdim=True))
+        return paddle.divide(input, input_norm)
+
+if __name__ == "__main__":
+    
+    import numpy as np
+    metric = MSMLoss(48)
+
+    #prepare data
+    np.random.seed(1)
+    features = np.random.randn(48, 32)
+    #print(features)
+
+    #do inference
+    features  = paddle.to_tensor(features)
+    loss = metric(features)
+    print(loss)
+    
\ No newline at end of file

ppcls/losses/npairsloss.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+
+class NpairsLoss(paddle.nn.Layer):
+    
+    def __init__(self, reg_lambda=0.01):
+        super(NpairsLoss, self).__init__()
+        self.reg_lambda = reg_lambda
+        
+    def forward(self, input, target=None):
+        """
+        anchor and positive(should include label)
+        """
+        features = input["features"]
+        reg_lambda = self.reg_lambda
+        batch_size = features.shape[0]
+        fea_dim    = features.shape[1]
+        num_class = batch_size // 2
+        
+        #reshape
+        out_feas = paddle.reshape(features, shape=[-1, 2, fea_dim])
+        anc_feas, pos_feas = paddle.split(out_feas, num_or_sections = 2, axis = 1)
+        anc_feas   = paddle.squeeze(anc_feas, axis=1)
+        pos_feas = paddle.squeeze(pos_feas, axis=1)
+        
+        #get simi matrix
+        similarity_matrix = paddle.matmul(anc_feas, pos_feas, transpose_y=True)     #get similarity matrix
+        sparse_labels = paddle.arange(0, num_class, dtype='int64')
+        xentloss = paddle.nn.CrossEntropyLoss()(similarity_matrix, sparse_labels)   #by default: mean
+        
+        #l2 norm
+        reg = paddle.mean(paddle.sum(paddle.square(features), axis=1))
+        l2loss = 0.5 * reg_lambda * reg
+        return {"npairsloss": xentloss + l2loss}
+    
+if __name__ == "__main__":
+    
+    import numpy as np
+    metric = NpairsLoss()
+
+    #prepare data
+    np.random.seed(1)
+    features = np.random.randn(160, 32)
+    #print(features)
+
+    #do inference
+    features  = paddle.to_tensor(features)
+    loss = metric(features)
+    print(loss)
+    
+

ppcls/losses/trihardloss.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from .comfunc import rerange_index
+
+class TriHardLoss(paddle.nn.Layer):
+    """
+    TriHard Loss, based on triplet loss. USE P * K samples.
+    the batch size is fixed. Batch_size = P * K;  but the K may vary between batches.
+    same label gather together
+    
+            supported_metrics = [
+            'euclidean',
+            'sqeuclidean',
+            'cityblock',
+        ]
+    only consider samples_each_class = 2
+    """
+    def __init__(self, batch_size = 120, samples_each_class=2,  margin=0.1):
+        super(TriHardLoss, self).__init__()
+        self.margin = margin
+        self.samples_each_class = samples_each_class
+        self.batch_size         = batch_size
+        self.rerange_index      = rerange_index(batch_size, samples_each_class)
+
+    def forward(self, input, target=None):
+        features = input["features"]
+        assert (self.batch_size == features.shape[0])
+        
+        #normalization 
+        features = self._nomalize(features)
+        samples_each_class = self.samples_each_class
+        rerange_index      = paddle.to_tensor(self.rerange_index)
+
+        #calc sm
+        diffs = paddle.unsqueeze(features, axis=1) - paddle.unsqueeze(features, axis=0)
+        similary_matrix =  paddle.sum(paddle.square(diffs), axis=-1)
+        
+        #rerange 
+        tmp = paddle.reshape(similary_matrix, shape = [-1, 1]) 
+        tmp = paddle.gather(tmp, index=rerange_index)   
+        similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])  
+        
+        #split
+        ignore, pos, neg = paddle.split(similary_matrix, num_or_sections= [1, 
+            samples_each_class - 1, -1], axis = 1)
+        
+        ignore.stop_gradient = True    
+        hard_pos = paddle.max(pos, axis=1) 
+        hard_neg = paddle.min(neg, axis=1)
+
+        loss = hard_pos + self.margin - hard_neg
+        loss = paddle.nn.ReLU()(loss)    
+        loss = paddle.mean(loss)
+        return {"trihardloss": loss}
+
+    def _nomalize(self, input):
+        input_norm = paddle.sqrt(paddle.sum(paddle.square(input), axis=1, keepdim=True))
+        return paddle.divide(input, input_norm)
+    
+if __name__ == "__main__":
+    
+    import numpy as np
+    metric = TriHardLoss(48)
+
+    #prepare data
+    np.random.seed(1)
+    features = np.random.randn(48, 32)
+    #print(features)
+
+    #do inference
+    features  = paddle.to_tensor(features)
+    loss = metric(features)
+    print(loss)
+
+
+

ppcls/losses/triplet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+
+class TripletLossV2(nn.Layer):
+    """Triplet loss with hard positive/negative mining.
+    Args:
+        margin (float): margin for triplet.
+    """
+    def __init__(self, margin=0.5):
+        super(TripletLossV2, self).__init__()
+        self.margin = margin
+        self.ranking_loss = paddle.nn.loss.MarginRankingLoss(margin=margin)
+
+    def forward(self, input, target, normalize_feature=True):
+        """
+        Args:
+            inputs: feature matrix with shape (batch_size, feat_dim)
+            target: ground truth labels with shape (num_classes)
+        """
+        inputs = input["features"]
+
+        if normalize_feature:
+            inputs = 1. * inputs / (paddle.expand_as(
+                paddle.norm(inputs, p=2, axis=-1, keepdim=True), inputs) +
+                                    1e-12)
+
+        bs = inputs.shape[0]
+
+        # compute distance
+        dist = paddle.pow(inputs, 2).sum(axis=1, keepdim=True).expand([bs, bs])
+        dist = dist + dist.t()
+        dist = paddle.addmm(input=dist,
+                            x=inputs,
+                            y=inputs.t(),
+                            alpha=-2.0,
+                            beta=1.0)
+        dist = paddle.clip(dist, min=1e-12).sqrt()
+
+        # hard negative mining
+        is_pos = paddle.expand(target, (bs, bs)).equal(
+            paddle.expand(target, (bs, bs)).t())
+        is_neg = paddle.expand(target, (bs, bs)).not_equal(
+            paddle.expand(target, (bs, bs)).t())
+
+        # `dist_ap` means distance(anchor, positive)
+        ## both `dist_ap` and `relative_p_inds` with shape [N, 1]
+        #print(is_pos.shape, dist.shape, type(is_pos), type(dist), paddle.reshape(paddle.masked_select(dist, is_pos),(bs, -1)))
+        '''
+        dist_ap, relative_p_inds = paddle.max(
+            paddle.reshape(dist[is_pos], (bs, -1)), axis=1, keepdim=True)
+        # `dist_an` means distance(anchor, negative)
+        # both `dist_an` and `relative_n_inds` with shape [N, 1]
+        dist_an, relative_n_inds = paddle.min(
+            paddle.reshape(dist[is_neg], (bs, -1)), axis=1, keepdim=True)
+        '''
+        dist_ap = paddle.max(paddle.reshape(paddle.masked_select(dist, is_pos),
+                                            (bs, -1)),
+                             axis=1,
+                             keepdim=True)
+        # `dist_an` means distance(anchor, negative)
+        # both `dist_an` and `relative_n_inds` with shape [N, 1]
+        dist_an = paddle.min(paddle.reshape(paddle.masked_select(dist, is_neg),
+                                            (bs, -1)),
+                             axis=1,
+                             keepdim=True)
+        # shape [N]
+        dist_ap = paddle.squeeze(dist_ap, axis=1)
+        dist_an = paddle.squeeze(dist_an, axis=1)
+
+        # Compute ranking hinge loss
+        y = paddle.ones_like(dist_an)
+        loss = self.ranking_loss(dist_an, dist_ap, y)
+        return {"TripletLossV2": loss}
+
+
+class TripletLoss(nn.Layer):
+    """Triplet loss with hard positive/negative mining.
+    Reference:
+    Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.
+    Code imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py.
+    Args:
+        margin (float): margin for triplet.
+    """
+    def __init__(self, margin=1.0):
+        super(TripletLoss, self).__init__()
+        self.margin = margin
+        self.ranking_loss = paddle.nn.loss.MarginRankingLoss(margin=margin)
+
+    def forward(self, input, target):
+        """
+        Args:
+            inputs: feature matrix with shape (batch_size, feat_dim)
+            target: ground truth labels with shape (num_classes)
+        """
+        inputs = input["features"]
+
+        #print(inputs.shape, targets.shape)
+        bs = inputs.shape[0]
+        # Compute pairwise distance, replace by the official when merged
+        dist = paddle.pow(inputs, 2).sum(axis=1, keepdim=True).expand([bs, bs])
+        dist = dist + dist.t()
+        dist = paddle.addmm(input=dist,
+                            x=inputs,
+                            y=inputs.t(),
+                            alpha=-2.0,
+                            beta=1.0)
+        dist = paddle.clip(dist, min=1e-12).sqrt()
+
+        mask = paddle.equal(target.expand([bs, bs]),
+                            target.expand([bs, bs]).t())
+        mask_numpy_idx = mask.numpy()
+        dist_ap, dist_an = [], []
+        for i in range(bs):
+            # dist_ap_i = paddle.to_tensor(dist[i].numpy()[mask_numpy_idx[i]].max(),dtype='float64').unsqueeze(0)
+            # dist_ap_i.stop_gradient = False
+            # dist_ap.append(dist_ap_i)
+            dist_ap.append(
+                max([
+                    dist[i][j]
+                    if mask_numpy_idx[i][j] == True else float("-inf")
+                    for j in range(bs)
+                ]).unsqueeze(0))
+            # dist_an_i = paddle.to_tensor(dist[i].numpy()[mask_numpy_idx[i] == False].min(), dtype='float64').unsqueeze(0)
+            # dist_an_i.stop_gradient = False
+            # dist_an.append(dist_an_i)
+            dist_an.append(
+                min([
+                    dist[i][k]
+                    if mask_numpy_idx[i][k] == False else float("inf")
+                    for k in range(bs)
+                ]).unsqueeze(0))
+
+        dist_ap = paddle.concat(dist_ap, axis=0)
+        dist_an = paddle.concat(dist_an, axis=0)
+
+        # Compute ranking hinge loss
+        y = paddle.ones_like(dist_an)
+        loss = self.ranking_loss(dist_an, dist_ap, y)
+        return {"TripletLoss": loss}